Ping Wang¹ HongGuo Cai^2,*, LuKun Wang³

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 519-529, 2020, DOI:10.32604/iasc.2020.013929

Abstract In order to improve the quality of intelligent English translation, an intelligent English translation algorithm based on the fuzzy semantic network is designed. By calculating the distance of fuzzy semantic network, classifying and ordering the English semantics to determine the optimal similarity and outputting the optimal translation results, the experiments show the average BLEU and NIST of the three test sets are 25.85 and 5.8925 respectively. The translation accuracy is higher than 95%. The algorithm can translate 246 Chinese sentences per second. This shows it is a high-performance intelligent translation algorithm and can be applied to practical intelligent translation software. More >

Farhan Ullah^1,4,*, Abdullah Bajahzar², Hamza Aldabbas³, Muhammad Farhan⁴, Hamad Naeem¹, S. Sabahat H. Bukhari^4,5, Kaleem Razzaq Malik⁶

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 169-180, 2020, DOI:10.31209/2019.100000138

Abstract This research aims to an electronic assessment (e-assessment) of students’ replies in response to the standard answer of teacher’s question to automate the assessment by WordNet semantic similarity. For this purpose, a new methodology for Semantic Similarity through WordNet Semantic Similarity Techniques (SS-WSST) has been proposed to calculate semantic similarity among teacher’ query and student’s reply. In the pilot study-1 42 words’ pairs extracted from 8 students’ replies, which marked by semantic similarity measures and compared with manually assigned teacher’s marks. The teacher is provided with 4 bins of the mark while our designed methodology provided an exact measure of… More >

Changrui Liu¹, Dengpan Ye^{1, *}, Yueyun Shang², Shunzhi Jiang¹, Shiyu Li¹, Yuan Mei¹, Liqiang Wang³

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1365-1386, 2020, DOI:10.32604/cmc.2020.07421

Abstract Image classifiers that based on Deep Neural Networks (DNNs) have been proved to be easily fooled by well-designed perturbations. Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers. In this paper, we propose a novel defense method which based on perceptual hash. Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense. To verify our idea, we defended against two main attack methods (a white-box attack and a black-box attack) in different DNN-based image classifiers and show that,… More >